Yearly Traffic Safety Analysis

629 CRASHES IN
BILLERICA, MA
2024

All metrics benchmarked against2023

In 2024, Billerica recorded 629 total crashes, a 12.1% increase from the 561 crashes documented in 2023. This rise in collisions was accompanied by a notable increase in crash severity, with total fatalities rising from one in the prior year to three in the current period.

629

12.1%was 561

Total Crash Events

3

200.0%was 1

Persons Killed

175

20.7%was 145

Persons Injured

52

57.6%was 33

Hit-and-Run Crashes

Note: "Persons Killed" (3) counts individual fatalities across all crash events. "Fatal" in the severity table below (3) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 9 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Crash data for Billerica indicates an upward trend year-over-year. Total crashes increased by 12.1%, from 561 in 2023 to 629 in 2024. This trend extends to crash outcomes, with total injuries rising by 20.7% from 145 to 175, and fatalities increasing from one to three.

52

Hit-and-Run Crashes — 2024

57.6% vs prior (33)

Hit-and-run incidents increased significantly year-over-year. The total number of hit-and-run crashes rose from 33 in 2023 to 52 in 2024, representing a 57.6% increase in count. The hit-and-run rate, or the percentage of total crashes classified as hit-and-run, also trended upward, increasing from 5.9% in the prior year to 8.3% in the current year.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 0%

0

Cyclists Killed

Prior: 00.0%

2

Motorists Killed

Prior: 1100.0%

0

Other Killed

Prior: 00.0%

3

Pedestrians Injured

Prior: 4-25.0%

1

Cyclists Injured

Prior: 10.0%

170

Motorists Injured

Prior: 13922.3%

1

Other Injured

Prior: 10.0%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes showed some shifts between the two periods. The peak day for crashes moved from Tuesday (105 crashes) in 2023 to Thursday (116 crashes) in 2024. However, the evening commute hour of 5 p.m. remained the single most frequent time for crashes in both years, with 56 incidents in 2023 and 58 in 2024.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity increased year-over-year. The number of fatal crashes rose from one in 2023 to three in 2024, with the fatal crash rate increasing from 0.2% to 0.5% of all incidents. The proportion of crashes involving serious injuries also grew, from 1.1% (6 crashes) to 1.7% (11 crashes), and the share of minor injury crashes increased from 9.8% to 13.0%.

Outcome by Severity (Crash Events)

Fatal3fatal crashes0.5%
200.0%prior 1
Serious Injury11serious injury crashes1.7%
83.3%prior 6
Minor Injury82minor injury crashes13%
49.1%prior 55
Possible Injury49possible injury crashes7.8%
-14.0%prior 57
No Injury475no injury crashes75.5%
8.7%prior 437

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Most severe injury per crash record

Top Contributing Factors

The top three contributing factors remained consistent across both years: 'Failed to yield right of way,' 'No improper driving,' and 'Followed too closely.' However, the number of crashes attributed to these factors changed. Crashes involving failure to yield increased by 18.8% in count (from 101 to 120), while those involving following too closely rose by 28.6% (from 70 to 90). Conversely, crashes related to 'Inattention' saw a 26.1% decrease in count, falling from 69 incidents in 2023 to 51 in 2024.

Officer-Reported Primary Contributing Cause

Failed to yield right of way120 (19.1%)18.8%prior 101
No improper driving100 (15.9%)7.5%prior 93
Followed too closely90 (14.3%)28.6%prior 70
Inattention51 (8.1%)-26.1%prior 69
Failure to keep in proper lane or running off road37 (5.9%)-26.0%prior 50
Disregarded traffic signs, signals, road markings33 (5.2%)65.0%prior 20
Driving too fast for conditions26 (4.1%)-13.3%prior 30
Other improper action18 (2.9%)38.5%prior 13
Made an improper turn16 (2.5%)77.8%prior 9
Distracted14 (2.2%)-6.7%prior 15

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across different environmental conditions remained largely stable year-over-year. In both 2024 and 2023, the majority of crashes occurred in daylight, on dry road surfaces, and in clear weather. For instance, crashes on dry roads accounted for 73.8% of the total in 2024, compared to 75.6% in 2023. Similarly, crashes in daylight represented 66.1% of incidents in 2024, a minimal change from 67.6% in the prior year, indicating no significant shift toward adverse-condition crashes.

Weather

Clear368 (59.5%)
12.2%prior 328
Cloudy58 (9.4%)
13.7%prior 51
Clear/Clear56 (9.1%)
-5.1%prior 59
Rain41 (6.6%)
17.1%prior 35
Snow22 (3.6%)
46.7%prior 15
Cloudy/Rain17 (2.8%)
13.3%prior 15
Rain/Cloudy14 (2.3%)
100.0%prior 7
Rain/Rain6 (1.0%)
-25.0%prior 8
Clear/Cloudy6 (1.0%)
Cloudy/Cloudy5 (0.8%)
-70.6%prior 17

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Weather condition at time of crash

Lighting

Daylight416 (66.1%)
9.8%prior 379
Dark - lighted roadway132 (21.0%)
21.1%prior 109
Dark - roadway not lighted38 (6.0%)
-15.6%prior 45
Dawn23 (3.7%)
130.0%prior 10
Dusk19 (3.0%)
35.7%prior 14
Dark - unknown roadway lighting1 (0.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Lighting condition field

Road Surface

Dry464 (74.4%)
9.4%prior 424
Wet109 (17.5%)
2.8%prior 106
Snow27 (4.3%)
50.0%prior 18
Ice19 (3.0%)
111.1%prior 9
Slush4 (0.6%)
Sand, mud, dirt, oil, gravel1 (0.2%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Toyota, Honda, and Ford being the most frequent in both periods. In 2024, Toyota (186 vehicles) surpassed Honda (162 vehicles) as the single most common make, whereas they were tied in 2023 (158 vehicles each). Regarding persons involved, the 26-34 age group was the largest cohort in both years. Notably, the number of persons in the 21-25 and 65+ age groups involved in crashes both increased from 126 to 167 year-over-year.

Top Vehicle Makes (1,150 vehicles)

1
TOYOTA186 (16.2%)
17.7%prior 158
2
HONDA162 (14.1%)
2.5%prior 158
3
FORD116 (10.1%)
4.5%prior 111
4
CHEVROLET95 (8.3%)
6.7%prior 89
5
NISSAN63 (5.5%)
18.9%prior 53
6
JEEP44 (3.8%)
-17.0%prior 53
7
SUBARU37 (3.2%)
-2.6%prior 38
8
GMC33 (2.9%)
22.2%prior 27
9
KIA31 (2.7%)
19.2%prior 26
10
HYUNDAI25 (2.2%)
-35.9%prior 39

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Vehicle unit records

95 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (1,334 persons with recorded sex)

Male782 (58.6%)
5.7%prior 740
Female550 (41.2%)
10.2%prior 499
X / Unspecified2 (0.1%)
0.0%prior 2

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes remained concentrated in 30 mph and 35 mph speed zones in both years. In 2024, 41.4% of crashes with a recorded speed limit occurred in 30 mph zones (214 crashes), up from a 36.5% share in 2023 (169 crashes). The three fatal crashes in 2024 were distributed across different speed zones, with one each occurring in 30 mph, 35 mph, and 65 mph zones. In the prior year, no fatal crashes were recorded in any of the listed speed zones.

Fatal crashes by zone: 30 mph: 1 of 214 (0.467%) · 35 mph: 1 of 175 (0.571%) · 65 mph: 1 of 38 (2.632%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2024-01-01 to 2024-12-31 · Posted speed limit at crash location

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: Arcgis_yearly Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2024-01-01 through 2024-12-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2024-01-01 through 2024-12-31 (366 days)
  • Geographic scope: BILLERICA, MA
  • Total crash records analyzed: 629
  • Total persons involved: 1,455
  • Total vehicles involved: 1,150

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "BILLERICA, MA Crash Intelligence Report: 2024." Published June 21, 2026. Reporting period: 2024-01-01 to 2024-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/billerica/2024-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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Billerica, MA Crash Report — 2024 | ThatCarHitMe.com